OpenRaiser/Intern-Atlas huggingface dataset:
The Impact of Social Media in Learning and Teaching: A Bibliometric-based Citation Analysis
Venue: arXiv cs.DL 2022
Year: 2022

This paper presents the results of a systematic review of the literature on the impact of social media in learning and teaching through bibliometric based Citation analysis. The objective of the review was to map the evolution of the current literature and identify the leading sources of knowledge in terms of the most influential journals, authors, and articles. From a total of 50 top most relevant articles selected from the Scopus database, a detailed citation analysis was conducted. The study explored the overall theoretical foundation of social media research involving in learning and studying and identified the leading sources of knowledge in terms of and papers and revealed research trends over the last four years by citation analysis. The analysis of citation data showed that International Journal of Management Education is the leading journal in social media in learning and teaching research. Author Abdullah Z was found to be the leading author in this field in terms of a total number of publications, total citations, and h index, while the most cited article was authored by Baaran S. and by Bapitha L. The contribution of this study is to clearly outline the current state of knowledge regarding social media in learning and teaching services in the literature.
A Framework for Single-Item NFT Auction Mechanism Design
Venue: arXiv cs.GT 2022
Year: 2022

Lately, Non-Fungible Tokens (NFTs), i.e., uniquely discernible assets on a blockchain, have skyrocketed in popularity by addressing a broad audience. However, the typical NFT auctioning procedures are conducted in various, ad hoc ways, while mostly ignoring the context that the blockchain provides. One of the main targets of this work is to shed light on the vastly unexplored design space of NFT Auction Mechanisms, especially in those characteristics that fundamentally differ from traditional and more contemporaneous forms of auctions. We focus on the case that bidders have a valuation for the auctioned NFT, i.e., what we term the single-item NFT auction case. In this setting, we formally define an NFT Auction Mechanism, give the properties that we would ideally like a perfect mechanism to satisfy (broadly known as incentive compatibility and collusion resistance) and prove that it is impossible to have such a perfect mechanism. Even though we cannot have an all-powerful protocol like that, we move on to consider relaxed notions of those properties that we may desire the protocol to satisfy, as a trade-off between implementability and economic guarantees. Specifically, we define the notion of an equilibrium-truthful auction, where neither the seller nor the bidders can improve their utility by acting non-truthfully, so long as the counter-party acts truthfully. We also define asymptotically second-price auctions, in which the seller does not lose asymptotically any revenue in comparison to the theoretically-optimal (static) second-price sealed-bid auction, in the case that the bidders' valuations are drawn independently from some distribution. We showcase why these two are very desirable properties for an auction mechanism to enjoy, and construct the first known NFT Auction Mechanism which provably possesses such formal guarantees.
Outage Probability Analysis of Mixed RF-FSO System Influenced by Fisher-Snedecor Fading and Gamma-Gamma Atmospheric Turbulence
Venue: arXiv cs.NI 2022
Year: 2022

In this paper, we investigate a dual-hop relaying system, composed of radio frequency (RF) and free-space optical (FSO) link. Decode-and-forward (DF) relay is employed to integrate the first RF link and the second line-of-sight FSO links. The RF channel is assumed to be subject to recently proposed Fisher-Snedecor fading model, which was shown to be convenient for modeling in realistic wireless communication scenarios. The FSO channel is affected by Gamma-Gamma distributed atmospheric turbulence. Expression for the outage probability is derived and utilized to present numerical results. Based on presented results, the effects of various RF and FSO channels parameters on the overall system performance are examined and discussed.
Deep Domain Adaptation for Detecting Bomb Craters in Aerial Images
Venue: arXiv cs.CV 2022
Year: 2022

The aftermath of air raids can still be seen for decades after the devastating events. Unexploded ordnance (UXO) is an immense danger to human life and the environment. Through the assessment of wartime images, experts can infer the occurrence of a dud. The current manual analysis process is expensive and time-consuming, thus automated detection of bomb craters by using deep learning is a promising way to improve the UXO disposal process. However, these methods require a large amount of manually labeled training data. This work leverages domain adaptation with moon surface images to address the problem of automated bomb crater detection with deep learning under the constraint of limited training data. This paper contributes to both academia and practice (1) by providing a solution approach for automated bomb crater detection with limited training data and (2) by demonstrating the usability and associated challenges of using synthetic images for domain adaptation.
An Investigation of the Bias-Variance Tradeoff in Meta-Gradients
Venue: arXiv cs.LG 2022
Year: 2022

Meta-gradients provide a general approach for optimizing the meta-parameters of reinforcement learning (RL) algorithms. Estimation of meta-gradients is central to the performance of these meta-algorithms, and has been studied in the setting of MAML-style short-horizon meta-RL problems. In this context, prior work has investigated the estimation of the Hessian of the RL objective, as well as tackling the problem of credit assignment to pre-adaptation behavior by making a sampling correction. However, we show that Hessian estimation, implemented for example by DiCE and its variants, always adds bias and can also add variance to meta-gradient estimation. Meanwhile, meta-gradient estimation has been studied less in the important long-horizon setting, where backpropagation through the full inner optimization trajectories is not feasible. We study the bias and variance tradeoff arising from truncated backpropagation and sampling correction, and additionally compare to evolution strategies, which is a recently popular alternative strategy to long-horizon meta-learning. While prior work implicitly chooses points in this bias-variance space, we disentangle the sources of bias and variance and present an empirical study that relates existing estimators to each other.
Colonoscopy Landmark Detection using Vision Transformers
Venue: arXiv cs.CV 2022
Year: 2022

Colonoscopy is a routine outpatient procedure used to examine the colon and rectum for any abnormalities including polyps, diverticula and narrowing of colon structures. A significant amount of the clinician's time is spent in post-processing snapshots taken during the colonoscopy procedure, for maintaining medical records or further investigation. Automating this step can save time and improve the efficiency of the process. In our work, we have collected a dataset of 120 colonoscopy videos and 2416 snapshots taken during the procedure, that have been annotated by experts. Further, we have developed a novel, vision-transformer based landmark detection algorithm that identifies key anatomical landmarks (the appendiceal orifice, ileocecal valve/cecum landmark and rectum retroflexion) from snapshots taken during colonoscopy. Our algorithm uses an adaptive gamma correction during preprocessing to maintain a consistent brightness for all images. We then use a vision transformer as the feature extraction backbone and a fully connected network based classifier head to categorize a given frame into four classes: the three landmarks or a non-landmark frame. We compare the vision transformer (ViT-B/16) backbone with ResNet-101 and ConvNext-B backbones that have been trained similarly. We report an accuracy of 82% with the vision transformer backbone on a test dataset of snapshots.
A domain adaptive deep learning solution for scanpath prediction of paintings
Venue: arXiv cs.CV 2022
Year: 2022

Cultural heritage understanding and preservation is an important issue for society as it represents a fundamental aspect of its identity. Paintings represent a significant part of cultural heritage, and are the subject of study continuously. However, the way viewers perceive paintings is strictly related to the so-called HVS (Human Vision System) behaviour. This paper focuses on the eye-movement analysis of viewers during the visual experience of a certain number of paintings. In further details, we introduce a new approach to predicting human visual attention, which impacts several cognitive functions for humans, including the fundamental understanding of a scene, and then extend it to painting images. The proposed new architecture ingests images and returns scanpaths, a sequence of points featuring a high likelihood of catching viewers' attention. We use an FCNN (Fully Convolutional Neural Network), in which we exploit a differentiable channel-wise selection and Soft-Argmax modules. We also incorporate learnable Gaussian distributions onto the network bottleneck to simulate visual attention process bias in natural scene images. Furthermore, to reduce the effect of shifts between different domains (i.e. natural images, painting), we urge the model to learn unsupervised general features from other domains using a gradient reversal classifier. The results obtained by our model outperform existing state-of-the-art ones in terms of accuracy and efficiency.
Oracle Analysis of Representations for Deep Open Set Detection
Venue: arXiv cs.CV 2022
Year: 2022

The problem of detecting a novel class at run time is known as Open Set Detection & is important for various real-world applications like medical application, autonomous driving, etc. Open Set Detection within context of deep learning involves solving two problems: (i) Must map the input images into a latent representation that contains enough information to detect the outliers, and (ii) Must learn an anomaly scoring function that can extract this information from the latent representation to identify the anomalies. Research in deep anomaly detection methods has progressed slowly. One reason may be that most papers simultaneously introduce new representation learning techniques and new anomaly scoring approaches. The goal of this work is to improve this methodology by providing ways of separately measuring the effectiveness of the representation learning and anomaly scoring. This work makes two methodological contributions. The first is to introduce the notion of Oracle anomaly detection for quantifying the information available in a learned latent representation. The second is to introduce Oracle representation learning, which produces a representation that is guaranteed to be sufficient for accurate anomaly detection. These two techniques help researchers to separate the quality of the learned representation from the performance of the anomaly scoring mechanism so that they can debug and improve their systems. The methods also provide an upper limit on how much open category detection can be improved through better anomaly scoring mechanisms. The combination of the two oracles gives an upper limit on the performance that any open category detection method could achieve. This work introduces these two oracle techniques and demonstrates their utility by applying them to several leading open category detection methods.
Software Sustainability: A Design Case for Achieving Sustainable Pension Services in Developing Country
Venue: arXiv cs.SE 2022
Year: 2022

The need for efficient and sustainable software to improve business and achieve goals cannot be over-emphasized. Sustainable digital services and product delivery cannot be achieved without embracing sustainable software design practices. Despite the current research progress on software sustainability, most software development practitioners in developing countries are unclear about what constitutes software sustainability and often lack the proper understanding of how to implement it in their specific industry domain. Research efforts from software engineering focused on promoting software sustainability awareness in developed countries, and fewer efforts have been channeled to studying the same awareness in developing countries. This has affected the level of awareness about sustainable software design practices in most developing countries. This research investigates the awareness of software sustainability in the Nigerian pension industry and its challenges among practitioners. The software development practitioners were engaged and interviewed. We offered ways to mitigate the identified challenges and promote the awareness of software sustainability in the pension industry. Our findings further show that, with the right sustainability knowledge, the software practitioners in the pension industry have the potential to support their organization's sustainable culture and improve the efficiency of product design and service delivery.
NasHD: Efficient ViT Architecture Performance Ranking using Hyperdimensional Computing
Venue: arXiv cs.CV 2022
Year: 2022

Neural Architecture Search (NAS) is an automated architecture engineering method for deep learning design automation, which serves as an alternative to the manual and error-prone process of model development, selection, evaluation and performance estimation. However, one major obstacle of NAS is the extremely demanding computation resource requirements and time-consuming iterations particularly when the dataset scales. In this paper, targeting at the emerging vision transformer (ViT), we present NasHD, a hyperdimensional computing based supervised learning model to rank the performance given the architectures and configurations. Different from other learning based methods, NasHD is faster thanks to the high parallel processing of HDC architecture. We also evaluated two HDC encoding schemes: Gram-based and Record-based of NasHD on their performance and efficiency. On the VIMER-UFO benchmark dataset of 8 applications from a diverse range of domains, NasHD Record can rank the performance of nearly 100K vision transformer models with about 1 minute while still achieving comparable results with sophisticated models.
Incorporation of Human Knowledge into Data Embeddings to Improve Pattern Significance and Interpretability
Venue: arXiv cs.HC 2022
Year: 2022

Embedding is a common technique for analyzing multi-dimensional data. However, the embedding projection cannot always form significant and interpretable visual structures that foreshadow underlying data patterns. We propose an approach that incorporates human knowledge into data embeddings to improve pattern significance and interpretability. The core idea is (1) externalizing tacit human knowledge as explicit sample labels and (2) adding a classification loss in the embedding network to encode samples' classes. The approach pulls samples of the same class with similar data features closer in the projection, leading to more compact (significant) and class-consistent (interpretable) visual structures. We give an embedding network with a customized classification loss to implement the idea and integrate the network into a visualization system to form a workflow that supports flexible class creation and pattern exploration. Patterns found on open datasets in case studies, subjects' performance in a user study, and quantitative experiment results illustrate the general usability and effectiveness of the approach.
Smart Active Sampling to enhance Quality Assurance Efficiency
Venue: arXiv cs.LG 2022
Year: 2022

We propose a new sampling strategy, called smart active sapling, for quality inspections outside the production line. Based on the principles of active learning a machine learning model decides which samples are sent to quality inspection. On the one hand, this minimizes the production of scrap parts due to earlier detection of quality violations. On the other hand, quality inspection costs are reduced for smooth operation.
Unsupervised Hashing with Semantic Concept Mining
Venue: arXiv cs.CV 2022
Year: 2022

Recently, to improve the unsupervised image retrieval performance, plenty of unsupervised hashing methods have been proposed by designing a semantic similarity matrix, which is based on the similarities between image features extracted by a pre-trained CNN model. However, most of these methods tend to ignore high-level abstract semantic concepts contained in images. Intuitively, concepts play an important role in calculating the similarity among images. In real-world scenarios, each image is associated with some concepts, and the similarity between two images will be larger if they share more identical concepts. Inspired by the above intuition, in this work, we propose a novel Unsupervised Hashing with Semantic Concept Mining, called UHSCM, which leverages a VLP model to construct a high-quality similarity matrix. Specifically, a set of randomly chosen concepts is first collected. Then, by employing a vision-language pretraining (VLP) model with the prompt engineering which has shown strong power in visual representation learning, the set of concepts is denoised according to the training images. Next, the proposed method UHSCM applies the VLP model with prompting again to mine the concept distribution of each image and construct a high-quality semantic similarity matrix based on the mined concept distributions. Finally, with the semantic similarity matrix as guiding information, a novel hashing loss with a modified contrastive loss based regularization item is proposed to optimize the hashing network. Extensive experiments on three benchmark datasets show that the proposed method outperforms the state-of-the-art baselines in the image retrieval task.
Combining Motion Matching and Orientation Prediction to Animate Avatars for Consumer-Grade VR Devices
Venue: arXiv cs.HC 2022
Year: 2022

The animation of user avatars plays a crucial role in conveying their pose, gestures, and relative distances to virtual objects or other users. Self-avatar animation in immersive VR helps improve the user experience and provides a Sense of Embodiment. However, consumer-grade VR devices typically include at most three trackers, one at the Head Mounted Display (HMD), and two at the handheld VR controllers. Since the problem of reconstruction the user pose from such sparse data is ill-defined, especially for the lower body, the approach adopted by most VR games consists of assuming the body orientation matches that of the HMD, and applying animation blending and time-warping from a reduced set of animations. Unfortunately, this approach produces noticeable mismatches between user and avatar movements. In this work we present a new approach to animate user avatars that is suitable for current mainstream VR devices. First, we use a neural network to estimate the user's body orientation based on the tracking information from the HMD and the hand controllers. Then we use this orientation together with the velocity and rotation of the HMD to build a feature vector that feeds a Motion Matching algorithm. We built a MoCap database with animations of VR users wearing a HMD and used it to test our approach on both self-avatars and other users' avatars. Our results show that our system can provide a large variety of lower body animations while correctly matching the user orientation, which in turn allows us to represent not only forward movements but also stepping in any direction.
Optimal Job Scheduling and Bandwidth Augmentation in Hybrid Data Center Networks
Venue: arXiv cs.DC 2022
Year: 2022

Optimizing data transfers is critical for improving job performance in data-parallel frameworks. In the hybrid data center with both wired and wireless links, reconfigurable wireless links can provide additional bandwidth to speed up job execution. However, it requires the scheduler and transceivers to make joint decisions under coupled constraints. In this work, we identify that the joint job scheduling and bandwidth augmentation problem is a complex mixed integer nonlinear problem, which is not solvable by existing optimization methods. To address this bottleneck, we transform it into an equivalent problem based on the coupling of its heuristic bounds, the revised data transfer representation and non-linear constraints decoupling and reformulation, such that the optimal solution can be efficiently acquired by the Branch and Bound method. Based on the proposed method, the performance of job scheduling with and without bandwidth augmentation is studied. Experiments show that the performance gain depends on multiple factors, especially the data size. Compared with existing solutions, our method can averagely reduce the job completion time by up to 10% under the setting of production scenario.
GIDP: Learning a Good Initialization and Inducing Descriptor Post-enhancing for Large-scale Place Recognition
Venue: arXiv cs.CV 2022
Year: 2022

Large-scale place recognition is a fundamental but challenging task, which plays an increasingly important role in autonomous driving and robotics. Existing methods have achieved acceptable good performance, however, most of them are concentrating on designing elaborate global descriptor learning network structures. The importance of feature generalization and descriptor post-enhancing has long been neglected. In this work, we propose a novel method named GIDP to learn a Good Initialization and Inducing Descriptor Poseenhancing for Large-scale Place Recognition. In particular, an unsupervised momentum contrast point cloud pretraining module and a reranking-based descriptor post-enhancing module are proposed respectively in GIDP. The former aims at learning a good initialization for the point cloud encoding network before training the place recognition model, while the later aims at post-enhancing the predicted global descriptor through reranking at inference time. Extensive experiments on both indoor and outdoor datasets demonstrate that our method can achieve state-of-the-art performance using simple and general point cloud encoding backbones.
Analysis on Blockchain Consensus Mechanism Based on Proof of Work and Proof of Stake
Venue: arXiv cs.CR 2022
Year: 2022

In the white book of Bitcion, Satoshi Nakamoto described a bitcoin system that can realize point-to-point online payment without a third-party organization. After supporting this magical application scenario and subverting the traditional centralized system, the blockchain technology has attracted worldwide attention, triggered a research upsurge of blockchain consensus algorithm, and produced a large number of innovative applications. Although various consensus algorithms continue to evolve with the iteration of blockchain products and applications, Proof of Work (POW) and Proof of Skake (POS) algorithms are still the core of consensus algorithms. This paper discusses two algorithms of POW and POS in blockchain consensus mechanism, and analyzes the advantages and the existing problems of the two consensus mechanisms. Since consensus mechanism is the main focus of blockchain technology and has many influencing factors, this paper discusses the current problems and some improved ideas, and selects some typical algorithms for a more systematic introduction. In addition, some important issues related to safety and performance are also discussed. This paper provides the researchers a great reference on blockchain consensus mechanism.